Multi-distribution learning generalizes the classic PAC learning to handle data coming from
multiple distributions. Given a set of $ k $ data distributions and a hypothesis class of VC …

Collaboration is crucial for reaching collective goals. However, its potential for effectiveness
is often undermined by the strategic behavior of individual agents—a fact that is captured by …

The goal of multi-objective optimization (MOO) is to learn under multiple, potentially
conflicting, objectives. One widely used technique to tackle MOO is through linear …

Despite recent advances in transfer learning with multiple source data sets, there still lacks
developments for mixture target populations that could be approximated through a …

Multi-distribution or collaborative learning involves learning a single predictor that works
well across multiple data distributions, using samples from each during training. Recent …

Modern challenges of robustness, fairness, and decision-making in machine learning have
led to the formulation of multi-distribution learning (MDL) frameworks in which a predictor is …

We introduce a fine-grained framework for uncertainty quantification of predictive models
under distributional shifts. This framework distinguishes the shift in covariate distributions …

Distributionally robust policy learning aims to find a policy that performs well under the worst-
case distributional shift, and yet most existing methods for robust policy learning consider …

In data-driven decision-making in marketing, healthcare, and education, it is desirable to
utilize a large amount of data from existing ventures to navigate high-dimensional feature …

Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize
multiple conflicting objectives, a challenge frequently encountered in areas like multi-task …